scholarly journals Heterogeneity of Spatial Distribution and Factors Influencing Unattended Locker Points in Guangzhou, China: The Case of Hive Box

2021 ◽  
Vol 10 (6) ◽  
pp. 409
Author(s):  
Song Liu ◽  
Ying Liu ◽  
Rongrong Zhang ◽  
Yongwang Cao ◽  
Ming Li ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Kernel Density ◽  
Global Perspective ◽  
Low Carbon ◽  
City Center ◽  
Geographic Variations ◽  
Community Based ◽  
Factors Influencing ◽  
End To End ◽  
The City

Hive Box is a company that operates a network of express unattended collection and delivery points (UCDPs) in China. Hive Box distribution enhances community-based end-to-end delivery services and low-carbon city logistics. It is argued that UCDPs compared with attended collection and delivery points (ACDPs) should be considered for further investigation. Therefore, the present study employed kernel density estimation, spatial autocorrelation analysis, and geographically weighted regression to investigate the spatial heterogeneity of Hive Box distribution across Guangzhou. Hive Box location data were collected from smartphone apps. The results were as follows: (1) the kernel density declined from the city center toward the outskirts, and showed point-like spatial agglomerations in the city center; (2) the Moran’s I index analysis showed that Hive Box distribution exhibited spatial agglomeration from a global perspective and geographic variations in locality in space; the heterogeneity of urban–rural differences implies the uneven development of Hive Box distribution in Guangzhou; and (3) the factors influencing Hive Box distribution were multilevel, and their effects were complex and varied across regions. These results shed light on the agglomeration and heterogeneity characteristics of the spatial distribution and influencing factors of Hive Boxes. For an enhanced community-based end-to-end delivery service, this study suggested the identification of the geographic variations of Hive Box distribution and the combined effects of multiple factors in intensifying the infrastructure of unattended locker points.

scholarly journals Analysis of the spatial distribution of dengue cases in the city of Rio de Janeiro, 2011 and 2012

2017 ◽  
Vol 51 ◽  
Author(s):  
Silvia Carvalho ◽  
Mônica De Avelar Figueiredo Mafra Magalhães ◽  
Roberto De Andrade Medronho
Keyword(s):  
Information System ◽  
Spatial Distribution ◽  
Rio De Janeiro ◽  
Influencing Factor ◽  
Kernel Density ◽  
Severe Dengue ◽  
Automatic Process ◽  
Density Estimator ◽  
Macroeconomic Policies ◽  
The City

OBJECTIVE Analyze the spatial distribution of classical dengue and severe dengue cases in the city of Rio de Janeiro. METHODS Exploratory study, considering cases of classical dengue and severe dengue with laboratory confirmation of the infection in the city of Rio de Janeiro during the years 2011/2012. The georeferencing technique was applied for the cases notified in the Notification Increase Information System in the period of 2011 and 2012. For this process, the fields “street” and “number” were used. The ArcGis10 program’s Geocoding tool’s automatic process was performed. The spatial analysis was done through the kernel density estimator. RESULTS Kernel density pointed out hotspots for classic dengue that did not coincide geographically with severe dengue and were in or near favelas. The kernel ratio did not show a notable change in the spatial distribution pattern observed in the kernel density analysis. The georeferencing process showed a loss of 41% of classic dengue registries and 17% of severe dengue registries due to the address in the Notification Increase Information System form. CONCLUSIONS The hotspots near the favelas suggest that the social vulnerability of these localities can be an influencing factor for the occurrence of this aggravation since there is a deficiency of the supply and access to essential goods and services for the population. To reduce this vulnerability, interventions must be related to macroeconomic policies.

scholarly journals Central Business District of St. Petersburg 1869–2017

2019 ◽  
Vol 3 (1) ◽  
pp. 23-39
Author(s):  
Константин Аркадьевич Холодилин ◽  
Леонид Эдуардович Лимонов
Keyword(s):  
Spatial Distribution ◽  
Market Economy ◽  
Substantial Reduction ◽  
Central Business District ◽  
Soviet Period ◽  
City Center ◽  
North East ◽  
The North ◽  
Business District ◽  
The City

The city center is at the core of urban and housing economics. Many models crucially depend on it. In a market economy, the location of urban amenities, especially eating establishments, closely correlates with that of the city center and, more generally, with the Central Business District (CBD). In a centrally planned economy, the spatial distribution of those amenities is determined by the central planner and can differ significantly from a market-based distribution. Using the case of St. Petersburg (Russia), we investigate changes in the spatial distribution of eating establishments resulting from the transition from a market economy to a centrally planned one and then again to a market economy. In addition, we explore the shifts of the city center between 1895 and 2017 using eating establishments as a proxy. The spatial distribution is analyzed using a 2-D kernel density estimation. We find evidence for a substantial reduction and dispersion of eating establishments during the Soviet period. We also establish that after the October 1917 Revolution the city center of St. Petersburg moved several kilometers to the north-east.

scholarly journals Analysis of Life Quality in a Tropical Mountain City Using a Multi-Criteria Geospatial Technique: A Case Study of Kandy City, Sri Lanka

2020 ◽  
Vol 12 (7) ◽  
pp. 2918 ◽  
Author(s):  
DMSLB Dissanayake ◽  
Takehiro Morimoto ◽  
Yuji Murayama ◽  
Manjula Ranagalage ◽  
ENC Perera
Keyword(s):  
Quality Of Life ◽  
Spatial Distribution ◽  
Sri Lanka ◽  
Distribution Pattern ◽  
Gradient Analysis ◽  
Life Quality ◽  
Spatial Distribution Pattern ◽  
City Center ◽  
The City

The blooming of urban expansion has led to the improvement of urban life, but some of the negative externalities have affected the life quality of urban dwellers, both directly and indirectly. As a result of this, research related to the quality of life has gained much attention among multidisciplinary researchers around the world. A number of attempts have been made by previous researchers to identify, assess, quantify, and map quality of life or well-being under various kinds of perspectives. The objectives of this research were to create a life quality index (LQI) and identify the spatial distribution pattern of LQI in Kandy City, Sri Lanka. Multiple factors were decomposed, a hierarchy was constructed by the multi-criteria decision making (MCDM) method, and 13 factors were selected under two main criteria—environmental and socioeconomic. Pairwise comparison matrices were created, and the weight of each factor was determined by the analytic hierarchy process (AHP). Finally, gradient analysis was employed to examine the spatial distribution pattern of LQI from the city center to the periphery. The results show that socioeconomic factors affect the quality of life more strongly than environmental factors, and the most significant factor is transportation. The highest life quality zones (26% of the total area) were distributed around the city center, while the lowest zones represented only 9% of the whole area. As shown in the gradient analysis, more than 50% of the land in the first five kilometers from the city center comes under the highest life quality zone. This research will provide guidance for the residents and respective administrative bodies to make Kandy City a livable city. It the constructed model can be applied to any geographical area by conducting necessary data calibration.

scholarly journals SPATIAL AND TEMPORAL CHANGE ANALYSIS OF URBAN HEAT ISLAND EFFECT IN WUHAN CITY

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 705-712
Author(s):  
M. H. Huang ◽  
J. J. Chen
Keyword(s):  
Spatial Distribution ◽  
Urban Heat Island ◽  
Vegetation Cover ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
City Center ◽  
Wuhan City ◽  
Island Effect ◽  
Urban Heat ◽  
The City

Abstract. China has experienced rapid urbanization and rapid development of economy in the past decades, resulting in severe damage to the urban ecological environment, causing changes in the urban thermal environment and triggering the urban heat island effect. Moreover, the heat island effect has become a hot topic for scholars. The urban heat island effect refers to the phenomenon that the urban surface temperature is significantly higher than that of surrounding suburbs due to the interaction of man-made and natural. The city is considered to be the largest man-made ecosystem. Its heat island effect will not only change the growth habit of urban vegetation， but also affect the outer environment of urban buildings, it further influences human life and has a great negative impact on human health. Therefore, the study of the spatial-temporal variation characteristics of urban heat island effect and its influencing factors can provide data support for the environmental quality control and urban planning of local government departments. Based on the surface temperature remote sensing product data, we studied the spatial distribution characteristics of urban heat island effect in Wuhan from 2001 to 2013, by calculating the temperature difference between the highest and lowest temperatures and the average interval method for heat island classification. We conducted a trend analysis of vegetation cover from 2001 to 2013 initially explore the effects of vegetation cover n heat island effect. The results showed that: (1) From 2001 to 2013, the intensity of heat island in Wuhan was strong in the city center, weaker surrounding city center and the weakest in the suburbs; From 2001 to 2011, the intensity of heat island in Wuhan city was significantly weaken, among which Huangpi, Xinzhou, Jiangxia, Hannan and Caidian district were weaken, and the urban heat island effect of the city center was enhanced; From 2011 to 2013, the intensity of heat island in Wuhan city presented an increasing trend, among which Huangpi district, Xinzhou district and Caidian district were the most obvious, and the urban heat island effect was slightly weaken. (2) Between 2001 and 2013, the vegetation cover in Huangpi district and Xinzhou district increased significantly, and the vegetation cover in the downtown, Jiangxia district and Dongxihu district decreased significantly, corresponding to the urban heat island effect of Wuhan increased volatility. Our results showed that the spatial distribution of urban heat island effect in Wuhan city fluctuated with time during the study period, and the vegetation cover had a significant influence on it.

scholarly journals Geographical index of concentration as an indicator of the spatial distribution of tourist attractions in Belgrade

Turizam ◽  
2021 ◽  
Vol 25 (1) ◽  
pp. 45-54
Author(s):  
Sanja Pavlović ◽  
Radmila Jovanović
Keyword(s):  
Spatial Distribution ◽  
Spatial Structure ◽  
Central City ◽  
Field Research ◽  
City Center ◽  
Tourist Attractions ◽  
Geographical Concentration ◽  
Structural Parts ◽  
Definition Of ◽  
The City

The spatial structure of tourist attractions can be presented both qualitatively and quantitatively. One of the indicators of the spatial structure of tourism is the index of geographical concentration of tourist attractions. The geographical concentration of tourist attractions represents the ratio of the number of tourist attractions in the observed area and its structural parts and the total number of structural units of the analyzed area. This paper aims to determine the spatial distribution of attractions in the administrative territories of Belgrade municipalities and to establish correlations with tourist attendance. The number and spatial distribution of accommodation capacities are the largest in the central city municipalities so that the number of visitors is the largest in them. At the same time, the central city municipalities have the highest concentration of tourist attractions. For data collection, the authors used field research, OSM (Open Street Maps), Google maps, with software processing ArcGIS 10.2. The research results enabled the definition of the model of distribution of tourist attractions and indicated its application. This model of distribution of tourist attractions shows that they are mostly concentrated in the city center. This also means a small spatial connection of tourist attractions in the city center and peripheral parts.

scholarly journals Price and Spatial Distribution of Office Rental in Madrid: A Decision Tree Analysis

Economía ◽  
2021 ◽  
Vol 44 (87) ◽  
pp. 20-40
Author(s):  
Máximo Camacho ◽  
Salvador Ramallo ◽  
Manuel Ruiz
Keyword(s):  
Spatial Distribution ◽  
Decision Tree ◽  
Decision Tree Analysis ◽  
Random Forest Algorithm ◽  
City Center ◽  
Tree Analysis ◽  
High Rent ◽  
Stratified Analysis ◽  
The City ◽  
Tree Approach

In this paper, we assess the drivers of office rental prices in the municipality of Madrid with a sample of 4,721 offices in March, 2020. The estimation was performed using the decision tree approach, which was built with a random forest algorithm. This technique allows us to capture the strong nonlinear component in the relation between price and its drivers, mainly geospatial location. Through a stratified analysis, we find out that the willingness to pay high rent in the center of Madrid is a feature of particular relevance to medium-sized offices. For diferent reasons, we also find out some office clusters located far from the city center with high rent for both large and small offices.

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